Biology subjects

Gandhi, K.

Publications and source records attributed to Gandhi, K..

4 recordsLinked to original sources

bioRxiv2025

Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans

We present a computational pipeline that links nuclear morphology to mRNA expression-based cell phenotypes under diverse biological conditions, including aging, disease progression, and drug response, using RNAscope imaging. The pipeline consists of three components: nuclear segmentation from RNAscope images, nuclear morphology identification, and downstream statistical analysis. Central to our approach is a novel unsupervised method, based on deep disentangled representation learning, which effectively captures diverse nuclear morphologies in large-scale datasets, as validated on synthetic benchmarks. We applied the full pipeline to RNAscope data targeting dopaminergic and glutamatergic neuron populations in the midbrains of mice and humans. Our analyses uncovered distinct nuclear morphology differences between dopaminergic and non-dopaminergic, as well as glutamatergic and non-glutamatergic neurons, in both species. Moreover, we identified a significant interaction between neurotransmitter identity and healthy aging in mice, reflected in systematic changes in nuclear morphology. These findings position nuclear morphology as a scalable and informative imaging-based readout of cell identity and physiological state.

bioinformatics↗

bioRxiv2025

Modulating p38MAPK recalibrates sensitivity of drug-resistant pancreatic adenoductal carcinoma cells toward chemotherapy and correlate with improved outcome in PDAC patients: Experimental and metadata evidence

Pancreatic cancer is one of the deadliest cancers and has very limited therapeutic options and a dismal prognosis. Among various signaling pathways which are activated during tumor development, hyperactivation of Mitogen-Activated Protein Kinase (MAPK) is responsible for high grade angiogenesis, polarization of Tumor Associated Macrophages, unfolded protein responses and exhaustion of T cells, which together contributes towards this therapeutic resistance. We therefore believe that MAPK targeting is expected to enhance sensitivity of highly resistant PDAC cells toward various cancer directed interventions. In this context, we investigated the impact of modulating p38MAPK on the sensitivity of pancreatic cancer cells towards gemcitabine. Supporting our hypothesis, our results convincely, demonstrated that indeed, p38 inhibition sensitizes both KRAS positive Panc-1 and MiaPaCa2 pancreatic carcinoma cells towards gemcitabine induced death. Interestingly p38MAPK targeting significantly reduced the cell viability, clonogenic potential of these cells and enhanced the early apoptosis. Our in-silico studies, supporting our in vitro data, potentially correlated that that high expression of p38 MAPK14 in PDAC patients is associated with poor prognosis and disease free survival. Deep miming of in silico data further demonstrated that MAPK14, in association with, hypoxia inducible factor-1 alpha and vascular endothelial growth factor signaling pathways promote angiogenic programming of PDAC which render these tumors refractory for cancer directed interventions. Based on our preliminary data, we believe that p38 MAPK based approach is potential approach for changing the faith of PDAC patients toward chemo and immunotherapy and believed to improve PDAC burden effectively in the host. HighlightsO_LIp38 MAPK inhibition enhances the sensitivity of KRAS+ pancreatic cancer cells for Gemcitabine C_LIO_LIP38MAPK knockdown cells are sensitive for Gemcitabine induced death C_LIO_LIMAPK14 (p38) is associated with angiogenesis and poor prognosis in pancreatic cancer C_LIO_LIMAPK14 Regulates Pro-Tumorigenic Pathways and immune infiltration in Pancreatic Cancer C_LI

cancer biology↗

bioRxiv2025

Valorization of Paneer waste Whey through Fermentation with Pediococcus pentosaceus NCDC 273 Insights from Intracellular Metabolomics by GC-MS

The present study employed an untargeted GC-MS-based metabolomics approach to investigate the intracellular metabolic landscape of Pediococcus pentosaceus NCDC 273 during the fermentation of paneer whey. The goal was to understand the metabolic dynamics that enable this bacterium to efficiently utilize a dairy by-product and produce value-added compounds. Intracellular metabolites were profiled at the early (6 h) and late (18 h) exponential growth phases, revealing a distinct temporal metabolic reprogramming. Multivariate analysis and correlation networks confirmed significant metabolic shifts, where the early phase was characterized by active uptake and utilization of diverse sugars and fatty acids to support rapid proliferation. In contrast, the late phase demonstrated a metabolic reorientation towards energy production, redox balance, and stress adaptation. This was evidenced by the significant accumulation of key metabolites such as lactic acid, nicotinamide, and trehalose, which are crucial for maintaining growth and tolerance in an acidifying environment. Overall, the findings demonstrate that P. pentosaceus NCDC 273 possesses the metabolic flexibility to effectively channel whey-derived sugars into central metabolism, while concurrently deploying adaptive strategies for survival. This research represents the first intracellular metabolomic characterization of this strain during whey fermentation, providing novel mechanistic insights that reinforce its potential for the valorization of paneer whey into functional bioproducts.

microbiology↗

bioRxiv2025

Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model

We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images, addressing key challenges such as large data volumes, low signal-to-noise ratios, and the heterogeneity of subcellular shapes and sizes. The method requires users to select a small number of slabs from a few representative tomograms in the dataset. It leverages features extracted from all layers of a Stable Diffusion foundation model, followed by a novel heuristic-based feature aggregation strategy. Segmentation masks are generated using adaptive thresholding, refined with CellPose to split composite regions, and then utilized as pseudo-ground truth for training deep learning models. We validated our pipeline on publicly available cryo-ET datasets of S. Pombe and C. Eleganscell sections, demonstrating performance that closely approximates expert human annotations. This fully automated, data-driven framework enables the mining of multi-scale subcellular patterns, paving the way for accelerated biological discoveries from large-scale cellular cryo-ET datasets.

bioinformatics↗